skip to main content
10.1145/3411763.3445016acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
course

Introduction to Explainable AI

Published: 08 May 2021 Publication History

Abstract

As Artificial Intelligence technologies are increasingly used to make important decisions and perform autonomous tasks, providing explanations to allow users and stakeholders to understand the AI has become a ubiquitous concern. Recently, a number of open-source toolkits are making the growing collection of Explainable AI (XAI) techniques accessible for researchers and practitioners to incorporate explanation features in AI systems. This course is open to anyone interested in implementing, designing and researching on the topic, aiming to provide an overview on the technical and design methods for XAI, as well as hands-on experience with an XAI toolkit.

References

[1]
Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. arXiv preprint arXiv:1909.03012(2019).
[2]
Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso. 2019. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics 8, 8 (2019), 832.
[3]
Jonathan Dodge, Q Vera Liao, Yunfeng Zhang, Rachel KE Bellamy, and Casey Dugan. 2019. Explaining models: an empirical study of how explanations impact fairness judgment. In Proceedings of the 24th International Conference on Intelligent User Interfaces. ACM, 275–285.
[4]
Malin Eiband, Hanna Schneider, Mark Bilandzic, Julian Fazekas-Con, Mareike Haug, and Heinrich Hussmann. 2018. Bringing transparency design into practice. In 23rd international conference on intelligent user interfaces. 211–223.
[5]
FICO. 2018. FICO Explainable Machine Learning Challenge. https://community.fico.com/s/explainable-machine-learning-challenge. Last accessed 2019-08.
[6]
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2019. A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51, 5 (2019), 93.
[7]
Q Vera Liao, Daniel Gruen, and Sarah Miller. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–15.
[8]
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019. Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1–15.

Cited By

View all
  • (2024)Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial IntelligenceElectronics10.3390/electronics1313243813:13(2438)Online publication date: 21-Jun-2024
  • (2024)XplAInable: Explainable AI Smoke Detection at the EdgeBig Data and Cognitive Computing10.3390/bdcc80500508:5(50)Online publication date: 17-May-2024
  • (2024)A Novel Metric for Evaluating the Stability of XAI ExplanationsAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0901139(133-142)Online publication date: Feb-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
May 2021
2965 pages
ISBN:9781450380959
DOI:10.1145/3411763
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2021

Check for updates

Author Tags

  1. AI
  2. explainable AI
  3. human-AI interaction
  4. interpretable machine learning
  5. machine learning

Qualifiers

  • Course
  • Research
  • Refereed limited

Conference

CHI '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)217
  • Downloads (Last 6 weeks)26
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial IntelligenceElectronics10.3390/electronics1313243813:13(2438)Online publication date: 21-Jun-2024
  • (2024)XplAInable: Explainable AI Smoke Detection at the EdgeBig Data and Cognitive Computing10.3390/bdcc80500508:5(50)Online publication date: 17-May-2024
  • (2024)A Novel Metric for Evaluating the Stability of XAI ExplanationsAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0901139(133-142)Online publication date: Feb-2024
  • (2024)User Characteristics in Explainable AI: The Rabbit Hole of Personalization?Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642352(1-13)Online publication date: 11-May-2024
  • (2024)Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Enhanced Deep Q-NetworkInternational Journal of Semantic Computing10.1142/S1793351X2444001X18:04(547-569)Online publication date: 20-Aug-2024
  • (2024)A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?IEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32664185:4(1429-1442)Online publication date: Apr-2024
  • (2024)Accessible Autonomous Vehicles as Symbiotic Autonomous Systems for Users with Disabilities: Preliminary Design Guidelines2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS59971.2024.10555877(1-5)Online publication date: 15-May-2024
  • (2024)Visioning a two-level human–machine communication framework: initiating conversations between explainable AI and communicationCommunication Theory10.1093/ct/qtae01634:4(216-229)Online publication date: 30-Jul-2024
  • (2024)Sustainable Development Goal 14: Explainable AI (XAI) for Ocean HealthArtificial Intelligence and Edge Computing for Sustainable Ocean Health10.1007/978-3-031-64642-3_8(167-198)Online publication date: 12-Sep-2024
  • (2023)Understanding Design Collaboration Between Designers and Artificial Intelligence: A Systematic Literature ReviewProceedings of the ACM on Human-Computer Interaction10.1145/36102177:CSCW2(1-35)Online publication date: 4-Oct-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media